import argparse import pprint from functools import partial from pathlib import Path from queue import Queue from threading import Thread from typing import Dict, List, Optional, Tuple, Union from torch import nn import h5py import torch from tqdm import tqdm from hloc import logger from hloc.utils.parsers import names_to_pair, names_to_pair_old, parse_retrieval from RDD.matchers import LightGlue class Matcher(nn.Module): default_conf = { "features": "rdd", "depth_confidence": 0.95, "width_confidence": 0.99, } required_inputs = [ "image0", "keypoints0", "descriptors0", "image1", "keypoints1", "descriptors1", ] def __init__(self, conf): super().__init__() self.net = LightGlue(conf.pop("features"), **conf) def forward(self, data): """Check the data and call the _forward method of the child model.""" for key in self.required_inputs: assert key in data, "Missing key {} in data".format(key) return self._forward(data) def _forward(self, data): data["descriptors0"] = data["descriptors0"].transpose(-1, -2) data["descriptors1"] = data["descriptors1"].transpose(-1, -2) return self.net( { "image0": {k[:-1]: v for k, v in data.items() if k[-1] == "0"}, "image1": {k[:-1]: v for k, v in data.items() if k[-1] == "1"}, } ) """ A set of standard configurations that can be directly selected from the command line using their name. Each is a dictionary with the following entries: - output: the name of the match file that will be generated. - model: the model configuration, as passed to a feature matcher. """ confs = { "rdd+lightglue": { "output": "matches-rdd-lightglue", "model": { "name": "lightglue", "features": "rdd", }, } } class WorkQueue: def __init__(self, work_fn, num_threads=1): self.queue = Queue(num_threads) self.threads = [ Thread(target=self.thread_fn, args=(work_fn,)) for _ in range(num_threads) ] for thread in self.threads: thread.start() def join(self): for thread in self.threads: self.queue.put(None) for thread in self.threads: thread.join() def thread_fn(self, work_fn): item = self.queue.get() while item is not None: work_fn(item) item = self.queue.get() def put(self, data): self.queue.put(data) class FeaturePairsDataset(torch.utils.data.Dataset): def __init__(self, pairs, feature_path_q, feature_path_r): self.pairs = pairs self.feature_path_q = feature_path_q self.feature_path_r = feature_path_r def __getitem__(self, idx): name0, name1 = self.pairs[idx] data = {} with h5py.File(self.feature_path_q, "r") as fd: grp = fd[name0] for k, v in grp.items(): data[k + "0"] = torch.from_numpy(v.__array__()).float() # some matchers might expect an image but only use its size data["image0"] = torch.empty((1,) + tuple(grp["image_size"])[::-1]) with h5py.File(self.feature_path_r, "r") as fd: grp = fd[name1] for k, v in grp.items(): data[k + "1"] = torch.from_numpy(v.__array__()).float() data["image1"] = torch.empty((1,) + tuple(grp["image_size"])[::-1]) return data def __len__(self): return len(self.pairs) def writer_fn(inp, match_path): pair, pred = inp with h5py.File(str(match_path), "a", libver="latest") as fd: if pair in fd: del fd[pair] grp = fd.create_group(pair) matches = pred["matches0"][0].cpu().short().numpy() grp.create_dataset("matches0", data=matches) if "matching_scores0" in pred: scores = pred["matching_scores0"][0].cpu().half().numpy() grp.create_dataset("matching_scores0", data=scores) def main( conf: Dict, pairs: Path, features: Union[Path, str], export_dir: Optional[Path] = None, matches: Optional[Path] = None, features_ref: Optional[Path] = None, overwrite: bool = False, device: str = "cpu", ) -> Path: if isinstance(features, Path) or Path(features).exists(): features_q = features if matches is None: raise ValueError( "Either provide both features and matches as Path" " or both as names." ) else: if export_dir is None: raise ValueError( "Provide an export_dir if features is not" f" a file path: {features}." ) features_q = Path(export_dir, features + ".h5") if matches is None: matches = Path(export_dir, f'{features}_{conf["output"]}_{pairs.stem}.h5') if features_ref is None: features_ref = features_q match_from_paths(conf, pairs, matches, features_q, features_ref, overwrite) return matches def find_unique_new_pairs(pairs_all: List[Tuple[str]], match_path: Path = None): """Avoid to recompute duplicates to save time.""" pairs = set() for i, j in pairs_all: if (j, i) not in pairs: pairs.add((i, j)) pairs = list(pairs) if match_path is not None and match_path.exists(): with h5py.File(str(match_path), "r", libver="latest") as fd: pairs_filtered = [] for i, j in pairs: if ( names_to_pair(i, j) in fd or names_to_pair(j, i) in fd or names_to_pair_old(i, j) in fd or names_to_pair_old(j, i) in fd ): continue pairs_filtered.append((i, j)) return pairs_filtered return pairs @torch.no_grad() def match_from_paths( conf: Dict, pairs_path: Path, match_path: Path, feature_path_q: Path, feature_path_ref: Path, overwrite: bool = False, ) -> Path: logger.info( "Matching local features with configuration:" f"\n{pprint.pformat(conf)}" ) device = 'cuda' if torch.cuda.is_available() else 'cpu' print(f"Using device: {device}") if not feature_path_q.exists(): raise FileNotFoundError(f"Query feature file {feature_path_q}.") if not feature_path_ref.exists(): raise FileNotFoundError(f"Reference feature file {feature_path_ref}.") match_path.parent.mkdir(exist_ok=True, parents=True) assert pairs_path.exists(), pairs_path pairs = parse_retrieval(pairs_path) pairs = [(q, r) for q, rs in pairs.items() for r in rs] pairs = find_unique_new_pairs(pairs, None if overwrite else match_path) if len(pairs) == 0: logger.info("Skipping the matching.") return model = Matcher(conf["model"]) model.eval() model.to(device) dataset = FeaturePairsDataset(pairs, feature_path_q, feature_path_ref) loader = torch.utils.data.DataLoader( dataset, num_workers=5, batch_size=1, shuffle=False, pin_memory=True ) writer_queue = WorkQueue(partial(writer_fn, match_path=match_path), 5) for idx, data in enumerate(tqdm(loader, smoothing=0.1)): data = { k: v if k.startswith("image") else v.to(device, non_blocking=True) for k, v in data.items() } pred = model(data) # if matches are less than 25 then skip pair = names_to_pair(*pairs[idx]) writer_queue.put((pair, pred)) writer_queue.join() logger.info("Finished exporting matches.") if __name__ == "__main__": parser = argparse.ArgumentParser() parser.add_argument("--pairs", type=Path, required=True) parser.add_argument("--export_dir", type=Path) parser.add_argument("--features", type=str, default="feats-superpoint-n4096-r1024") parser.add_argument("--matches", type=Path) parser.add_argument( "--conf", type=str, default="superglue", choices=list(confs.keys()) ) args = parser.parse_args() main(confs[args.conf], args.pairs, args.features, args.export_dir)